As a species, we place an especial emphasis on plans and strategies. Moreover, we feel that it is vitally important to choose the right ones.
That could be because, as a species, we seem to be a bit rubbish at that. Knowing when to quit, however, is actually a good deal harder.
Particularly in business, unforced errors come in two broad categories. The first – and most obvious – is picking the wrong strategy. That’s a bit of a no-brainer, really. A bad strategy doesn’t get you to where you want to go within the planned amounts of time or money.
The second, rather less obvious, issue is the failure to correctly reassess a plan.
Difficulties may arise, and circumstances may change, and assorted problems, delays or issues might crop up. Good strategies are often abandoned for no other reason than that things got tough, where otherwise simple determination might see you through to a successful end.
Likewise, some people hang on to a plan that is clearly not going to work out, and instead of stepping back, reassessing and formulating a new strategy, the old plan is followed to bitter failure.
However rubbish we might all be at picking successful strategies, we’re actually far worse at knowing when to quit, and when to stay the course.
Proverbial old sayings include “Quitters never win, and winners never quit.” It’s delightful in its rhythm and symmetry, and like many such platitudes, not actually supported by any statistical fact. It’s ideas like that – designed to tackle the issue of those who admit defeat too early – that all-too-often lead to the demise of projects by quitting them far too late.
Knowing when to quit, and when to stay the course on a plan is a tough judgement call. It’s done best when you have all of the data, you understand that data, and you’re able to think about it with a relative minimum of subjectivity.
It’s easy to do the whole armchair quarterback thing, and kibitz based on an incomplete understanding of what’s going on, telling someone else that they’re making the wrong play. It’s possible that their data isn’t actually any better than yours. That’s just not the way to bet.
Not unless you’ve got good cause to believe that the data’s faulty. Just don’t expect to be heeded.
In the final analysis, however, knowing when to quit and when not to quit is hard. It isn’t surprising that it is one of the things we’re all most likely to get wrong.